import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns

# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os

activity = pd.read_csv('FitBit data.csv') # importing the dataset

import pandas_profiling

activity.profile_report() # seeing the full report about the dataset

activity.shape # checking the number of rows and columns in the dataset

(457, 15)

activity.isnull().sum() # checking the number of missing values in the dataset

Id                          0
ActivityDate                0
TotalSteps                  0
TotalDistance               0
TrackerDistance             0
LoggedActivitiesDistance    0
VeryActiveDistance          0
ModeratelyActiveDistance    0
LightActiveDistance         0
SedentaryActiveDistance     0
VeryActiveMinutes           0
FairlyActiveMinutes         0
LightlyActiveMinutes        0
SedentaryMinutes            0
Calories                    0
dtype: int64

activity.head(10) # seeing a sample of 10 rows from the dataset

	Id	ActivityDate	TotalSteps	TotalDistance	TrackerDistance	LoggedActivitiesDistance	VeryActiveDistance	ModeratelyActiveDistance	LightActiveDistance	SedentaryActiveDistance	VeryActiveMinutes	FairlyActiveMinutes	LightlyActiveMinutes	SedentaryMinutes	Calories
0	1503960366	3/25/2016	11004	7.11	7.11	0.0	2.57	0.46	4.07	0.0	33	12	205	804	1819
1	1503960366	3/26/2016	17609	11.55	11.55	0.0	6.92	0.73	3.91	0.0	89	17	274	588	2154
2	1503960366	3/27/2016	12736	8.53	8.53	0.0	4.66	0.16	3.71	0.0	56	5	268	605	1944
3	1503960366	3/28/2016	13231	8.93	8.93	0.0	3.19	0.79	4.95	0.0	39	20	224	1080	1932
4	1503960366	3/29/2016	12041	7.85	7.85	0.0	2.16	1.09	4.61	0.0	28	28	243	763	1886
5	1503960366	3/30/2016	10970	7.16	7.16	0.0	2.36	0.51	4.29	0.0	30	13	223	1174	1820
6	1503960366	3/31/2016	12256	7.86	7.86	0.0	2.29	0.49	5.04	0.0	33	12	239	820	1889
7	1503960366	4/1/2016	12262	7.87	7.87	0.0	3.32	0.83	3.64	0.0	47	21	200	866	1868
8	1503960366	4/2/2016	11248	7.25	7.25	0.0	3.00	0.45	3.74	0.0	40	11	244	636	1843
9	1503960366	4/3/2016	10016	6.37	6.37	0.0	0.91	1.28	4.18	0.0	15	30	314	655	1850

activity1 = activity.copy() # copying the datset to activity1

activity1['ActivityDate'].unique() # checking out the unique activity dates in the dataset

array(['3/25/2016', '3/26/2016', '3/27/2016', '3/28/2016', '3/29/2016',
       '3/30/2016', '3/31/2016', '4/1/2016', '4/2/2016', '4/3/2016',
       '4/4/2016', '4/5/2016', '4/6/2016', '4/7/2016', '4/8/2016',
       '4/9/2016', '4/10/2016', '4/11/2016', '4/12/2016', '3/12/2016',
       '3/13/2016', '3/14/2016', '3/15/2016', '3/16/2016', '3/17/2016',
       '3/18/2016', '3/19/2016', '3/20/2016', '3/21/2016', '3/22/2016',
       '3/23/2016', '3/24/2016'], dtype=object)

activity1['ActivityDate'].head(10)  # cheking out the datset before transformation

0    3/25/2016
1    3/26/2016
2    3/27/2016
3    3/28/2016
4    3/29/2016
5    3/30/2016
6    3/31/2016
7     4/1/2016
8     4/2/2016
9     4/3/2016
Name: ActivityDate, dtype: object

# adding the yearm month and date columns to the dataset
activity1['year'] = pd.DatetimeIndex(activity1['ActivityDate']).year
activity1['month'] = pd.DatetimeIndex(activity1['ActivityDate']).month
activity1['date'] = pd.DatetimeIndex(activity1['ActivityDate']).day

activity1.head(10) # cheking out the datset after transformation

	Id	ActivityDate	TotalSteps	TotalDistance	TrackerDistance	LoggedActivitiesDistance	VeryActiveDistance	ModeratelyActiveDistance	LightActiveDistance	SedentaryActiveDistance	VeryActiveMinutes	FairlyActiveMinutes	LightlyActiveMinutes	SedentaryMinutes	Calories	year	month	date
0	1503960366	3/25/2016	11004	7.11	7.11	0.0	2.57	0.46	4.07	0.0	33	12	205	804	1819	2016	3	25
1	1503960366	3/26/2016	17609	11.55	11.55	0.0	6.92	0.73	3.91	0.0	89	17	274	588	2154	2016	3	26
2	1503960366	3/27/2016	12736	8.53	8.53	0.0	4.66	0.16	3.71	0.0	56	5	268	605	1944	2016	3	27
3	1503960366	3/28/2016	13231	8.93	8.93	0.0	3.19	0.79	4.95	0.0	39	20	224	1080	1932	2016	3	28
4	1503960366	3/29/2016	12041	7.85	7.85	0.0	2.16	1.09	4.61	0.0	28	28	243	763	1886	2016	3	29
5	1503960366	3/30/2016	10970	7.16	7.16	0.0	2.36	0.51	4.29	0.0	30	13	223	1174	1820	2016	3	30
6	1503960366	3/31/2016	12256	7.86	7.86	0.0	2.29	0.49	5.04	0.0	33	12	239	820	1889	2016	3	31
7	1503960366	4/1/2016	12262	7.87	7.87	0.0	3.32	0.83	3.64	0.0	47	21	200	866	1868	2016	4	1
8	1503960366	4/2/2016	11248	7.25	7.25	0.0	3.00	0.45	3.74	0.0	40	11	244	636	1843	2016	4	2
9	1503960366	4/3/2016	10016	6.37	6.37	0.0	0.91	1.28	4.18	0.0	15	30	314	655	1850	2016	4	3

activity1=activity1.drop(['TrackerDistance'],axis=1)  #dropping the TrackerDistance column

activity1.head(200) # cheking out the first 200 rows of the datset after transformation

	Id	ActivityDate	TotalSteps	TotalDistance	LoggedActivitiesDistance	VeryActiveDistance	ModeratelyActiveDistance	LightActiveDistance	SedentaryActiveDistance	VeryActiveMinutes	FairlyActiveMinutes	LightlyActiveMinutes	SedentaryMinutes	Calories	year	month	date
0	1503960366	3/25/2016	11004	7.11	0.0	2.57	0.46	4.07	0.00	33	12	205	804	1819	2016	3	25
1	1503960366	3/26/2016	17609	11.55	0.0	6.92	0.73	3.91	0.00	89	17	274	588	2154	2016	3	26
2	1503960366	3/27/2016	12736	8.53	0.0	4.66	0.16	3.71	0.00	56	5	268	605	1944	2016	3	27
3	1503960366	3/28/2016	13231	8.93	0.0	3.19	0.79	4.95	0.00	39	20	224	1080	1932	2016	3	28
4	1503960366	3/29/2016	12041	7.85	0.0	2.16	1.09	4.61	0.00	28	28	243	763	1886	2016	3	29
5	1503960366	3/30/2016	10970	7.16	0.0	2.36	0.51	4.29	0.00	30	13	223	1174	1820	2016	3	30
6	1503960366	3/31/2016	12256	7.86	0.0	2.29	0.49	5.04	0.00	33	12	239	820	1889	2016	3	31
7	1503960366	4/1/2016	12262	7.87	0.0	3.32	0.83	3.64	0.00	47	21	200	866	1868	2016	4	1
8	1503960366	4/2/2016	11248	7.25	0.0	3.00	0.45	3.74	0.00	40	11	244	636	1843	2016	4	2
9	1503960366	4/3/2016	10016	6.37	0.0	0.91	1.28	4.18	0.00	15	30	314	655	1850	2016	4	3
10	1503960366	4/4/2016	14557	9.80	0.0	3.39	0.70	5.69	0.00	43	18	285	757	2030	2016	4	4
11	1503960366	4/5/2016	14844	9.73	0.0	2.94	0.76	6.04	0.00	36	18	341	736	2083	2016	4	5
12	1503960366	4/6/2016	11974	7.67	0.0	2.04	0.48	5.15	0.00	27	12	228	1173	1861	2016	4	6
13	1503960366	4/7/2016	10198	6.44	0.0	1.20	0.79	4.45	0.00	17	20	195	1208	1755	2016	4	7
14	1503960366	4/8/2016	12521	7.94	0.0	3.31	0.90	3.74	0.00	46	22	212	1160	1895	2016	4	8
15	1503960366	4/9/2016	12432	8.10	0.0	2.59	0.59	4.92	0.00	32	15	248	738	1883	2016	4	9
16	1503960366	4/10/2016	10057	6.98	0.0	4.00	0.49	2.48	0.00	44	13	168	737	1755	2016	4	10
17	1503960366	4/11/2016	10990	7.26	0.0	2.04	0.57	4.65	0.00	26	14	216	855	1811	2016	4	11
18	1503960366	4/12/2016	224	0.14	0.0	0.00	0.00	0.13	0.00	0	0	9	32	50	2016	4	12
19	1624580081	3/25/2016	1810	1.18	0.0	0.00	0.00	1.13	0.01	0	0	121	1319	1373	2016	3	25
20	1624580081	3/26/2016	815	0.53	0.0	0.00	0.00	0.53	0.00	0	0	47	1393	1264	2016	3	26
21	1624580081	3/27/2016	1985	1.29	0.0	0.00	0.00	1.28	0.01	0	0	112	1328	1350	2016	3	27
22	1624580081	3/28/2016	1905	1.24	0.0	0.00	0.00	1.24	0.00	0	0	95	1345	1342	2016	3	28
23	1624580081	3/29/2016	1552	1.01	0.0	0.00	0.00	1.00	0.00	0	0	66	1374	1300	2016	3	29
24	1624580081	3/30/2016	1675	1.09	0.0	0.00	0.00	1.09	0.00	0	0	84	1356	1313	2016	3	30
25	1624580081	3/31/2016	4506	2.93	0.0	0.51	0.27	2.15	0.01	7	4	144	1285	1498	2016	3	31
26	1624580081	4/1/2016	9218	5.99	0.0	0.00	0.00	5.97	0.01	0	0	221	1219	1541	2016	4	1
27	1624580081	4/2/2016	1556	1.01	0.0	0.00	0.00	1.01	0.01	0	0	88	1352	1327	2016	4	2
28	1624580081	4/3/2016	2910	1.89	0.0	0.00	0.00	1.88	0.01	0	0	157	1283	1452	2016	4	3
29	1624580081	4/4/2016	18464	12.00	0.0	0.00	0.00	12.00	0.00	0	0	270	1170	1574	2016	4	4
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
170	4020332650	3/17/2016	8940	6.41	0.0	0.00	0.00	0.61	0.04	0	0	47	986	3706	2016	3	17
171	4020332650	3/18/2016	368	0.26	0.0	0.00	0.00	0.00	0.00	0	0	0	1440	2024	2016	3	18
172	4020332650	3/19/2016	5702	4.09	0.0	0.00	0.00	0.00	0.00	0	0	0	1440	2980	2016	3	19
173	4020332650	3/20/2016	10330	7.41	0.0	0.00	0.00	0.00	0.00	0	0	0	1440	3728	2016	3	20
174	4020332650	3/21/2016	8778	6.29	0.0	0.00	0.00	0.00	0.00	0	0	0	1440	3348	2016	3	21
175	4020332650	3/22/2016	6662	4.78	0.0	0.00	0.00	0.00	0.00	0	0	0	1440	3162	2016	3	22
176	4020332650	3/23/2016	6309	4.52	0.0	0.00	0.00	0.00	0.00	0	0	0	1440	3140	2016	3	23
177	4020332650	3/24/2016	1951	1.41	0.0	0.00	0.00	0.00	0.00	0	0	0	1440	1980	2016	3	24
178	4020332650	3/25/2016	5563	3.99	0.0	0.00	0.00	0.00	0.00	0	0	0	1440	3681	2016	3	25
179	4020332650	3/26/2016	4370	3.13	0.0	0.00	0.00	0.00	0.00	0	0	0	1440	3444	2016	3	26
180	4020332650	3/27/2016	7144	5.12	0.0	0.00	0.00	0.00	0.00	0	0	0	1440	3510	2016	3	27
181	4020332650	3/28/2016	2106	1.51	0.0	0.02	0.05	0.21	0.00	31	45	75	1289	4010	2016	3	28
182	4020332650	3/29/2016	4152	2.98	0.0	0.00	0.00	2.96	0.00	0	0	187	1253	2976	2016	3	29
183	4020332650	3/30/2016	5400	3.87	0.0	0.00	0.00	3.85	0.00	0	0	258	795	3418	2016	3	30
184	4020332650	3/31/2016	7428	5.33	0.0	0.87	0.92	3.51	0.00	18	33	261	860	3439	2016	3	31
185	4020332650	4/1/2016	5351	3.84	0.0	0.62	0.18	3.01	0.00	15	17	210	748	3338	2016	4	1
186	4020332650	4/2/2016	4299	3.10	0.0	0.09	0.21	2.74	0.04	3	15	162	752	2892	2016	4	2
187	4020332650	4/3/2016	6107	4.38	0.0	0.00	0.00	4.36	0.00	0	0	265	776	3313	2016	4	3
188	4020332650	4/4/2016	6429	4.60	0.0	0.00	0.95	3.65	0.00	0	28	193	741	3118	2016	4	4
189	4020332650	4/5/2016	6880	4.93	0.0	0.50	0.76	3.65	0.00	7	19	165	1249	2966	2016	4	5
190	4020332650	4/6/2016	7476	5.36	0.0	0.00	0.00	5.31	0.00	0	0	263	920	3253	2016	4	6
191	4020332650	4/7/2016	6581	4.72	0.0	0.00	0.29	4.39	0.00	0	8	241	1191	3032	2016	4	7
192	4020332650	4/8/2016	10480	7.51	0.0	0.41	1.47	5.01	0.04	6	37	272	1125	4034	2016	4	8
193	4020332650	4/9/2016	7734	5.55	0.0	1.16	0.49	3.82	0.06	20	12	223	1185	3264	2016	4	9
194	4020332650	4/10/2016	5129	3.68	0.0	0.18	0.24	3.21	0.01	5	7	176	1178	2817	2016	4	10
195	4020332650	4/11/2016	2993	2.15	0.0	0.00	0.00	2.09	0.00	0	0	114	888	2507	2016	4	11
196	4020332650	4/12/2016	8	0.01	0.0	0.00	0.00	0.01	0.00	0	0	1	321	446	2016	4	12
197	4057192912	3/12/2016	0	0.00	0.0	0.00	0.00	0.00	0.00	0	0	0	1440	1777	2016	3	12
198	4057192912	3/13/2016	0	0.00	0.0	0.00	0.00	0.00	0.00	0	0	0	1440	1777	2016	3	13
199	4057192912	3/14/2016	8433	6.23	0.0	2.45	0.33	3.44	0.00	30	7	135	1268	2453	2016	3	14

200 rows × 17 columns

### Groupby the day of the month and make a boxplot of calories burnt
import matplotlib.pyplot as plt
# figure size
plt.figure(figsize=(15,8))

# Usual boxplot
ax = sns.boxplot(x='date', y='Calories', data=activity1)
 
# Add jitter with the swarmplot function.
ax = sns.swarmplot(x='date', y='Calories', data=activity1, color="grey")

ax.set_title('Box plot of Calories with Jitter bu day of the month')

Text(0.5, 1.0, 'Box plot of Calories with Jitter bu day of the month')

# converting the datatype to datetime
activity1['Week'] = pd.to_datetime(activity1.ActivityDate).dt.week
activity1['Year'] = pd.to_datetime(activity1.ActivityDate).dt.year

activity1.head()  # cheking out the datset after transformation

	Id	ActivityDate	TotalSteps	TotalDistance	LoggedActivitiesDistance	VeryActiveDistance	ModeratelyActiveDistance	LightActiveDistance	SedentaryActiveDistance	VeryActiveMinutes	FairlyActiveMinutes	LightlyActiveMinutes	SedentaryMinutes	Calories	year	month	date	Week	Year
0	1503960366	3/25/2016	11004	7.11	0.0	2.57	0.46	4.07	0.0	33	12	205	804	1819	2016	3	25	12	2016
1	1503960366	3/26/2016	17609	11.55	0.0	6.92	0.73	3.91	0.0	89	17	274	588	2154	2016	3	26	12	2016
2	1503960366	3/27/2016	12736	8.53	0.0	4.66	0.16	3.71	0.0	56	5	268	605	1944	2016	3	27	12	2016
3	1503960366	3/28/2016	13231	8.93	0.0	3.19	0.79	4.95	0.0	39	20	224	1080	1932	2016	3	28	13	2016
4	1503960366	3/29/2016	12041	7.85	0.0	2.16	1.09	4.61	0.0	28	28	243	763	1886	2016	3	29	13	2016

activity1.ActivityDate.dtype # cheking the datatype of ActivityDate field

dtype('O')

activity1['ActivityDate'] = pd.to_datetime(activity1['ActivityDate']) # converting it to datetime

activity1['day'] = activity1['ActivityDate'].dt.weekday_name # converting the day of the week to the name of the day

activity1.head(10) # cheking out the datset after transformation

	Id	ActivityDate	TotalSteps	TotalDistance	LoggedActivitiesDistance	VeryActiveDistance	ModeratelyActiveDistance	LightActiveDistance	SedentaryActiveDistance	VeryActiveMinutes	FairlyActiveMinutes	LightlyActiveMinutes	SedentaryMinutes	Calories	year	month	date	Week	Year	day
0	1503960366	2016-03-25	11004	7.11	0.0	2.57	0.46	4.07	0.0	33	12	205	804	1819	2016	3	25	12	2016	Friday
1	1503960366	2016-03-26	17609	11.55	0.0	6.92	0.73	3.91	0.0	89	17	274	588	2154	2016	3	26	12	2016	Saturday
2	1503960366	2016-03-27	12736	8.53	0.0	4.66	0.16	3.71	0.0	56	5	268	605	1944	2016	3	27	12	2016	Sunday
3	1503960366	2016-03-28	13231	8.93	0.0	3.19	0.79	4.95	0.0	39	20	224	1080	1932	2016	3	28	13	2016	Monday
4	1503960366	2016-03-29	12041	7.85	0.0	2.16	1.09	4.61	0.0	28	28	243	763	1886	2016	3	29	13	2016	Tuesday
5	1503960366	2016-03-30	10970	7.16	0.0	2.36	0.51	4.29	0.0	30	13	223	1174	1820	2016	3	30	13	2016	Wednesday
6	1503960366	2016-03-31	12256	7.86	0.0	2.29	0.49	5.04	0.0	33	12	239	820	1889	2016	3	31	13	2016	Thursday
7	1503960366	2016-04-01	12262	7.87	0.0	3.32	0.83	3.64	0.0	47	21	200	866	1868	2016	4	1	13	2016	Friday
8	1503960366	2016-04-02	11248	7.25	0.0	3.00	0.45	3.74	0.0	40	11	244	636	1843	2016	4	2	13	2016	Saturday
9	1503960366	2016-04-03	10016	6.37	0.0	0.91	1.28	4.18	0.0	15	30	314	655	1850	2016	4	3	13	2016	Sunday

# figure size
plt.figure(figsize=(15,8))

# simple barplot
ax = sns.barplot(x='day', y='Calories',  data=activity1)

ax.set_title('Barplot of calories by the day of the week')

Text(0.5, 1.0, 'Barplot of calories by the day of the week')

# figure size
plt.figure(figsize=(15,8))

# Simple scatterplot
ax = sns.scatterplot(x='Calories', y='SedentaryMinutes', data=activity1)

ax.set_title('Scatterplot of calories and intense_activities')

Text(0.5, 1.0, 'Scatterplot of calories and intense_activities')

# figure size
plt.figure(figsize=(15,8))

# Simple scatterplot
ax = sns.scatterplot(x='Calories', y='LightlyActiveMinutes', data=activity1)

ax.set_title('Scatterplot of calories and intense_activities')

Text(0.5, 1.0, 'Scatterplot of calories and intense_activities')

# figure size
plt.figure(figsize=(15,8))

# Simple scatterplot between calories burnt in the moderately active minutes
ax = sns.scatterplot(x='Calories', y='FairlyActiveMinutes', data=activity1)

ax.set_title('Scatterplot of calories vs Fairly Active Minutes')

Text(0.5, 1.0, 'Scatterplot of calories vs Fairly Active Minutes')

# figure size
plt.figure(figsize=(15,8))

# Simple scatterplot between calories burnt in the intensely active minutes
ax = sns.scatterplot(x='Calories', y='VeryActiveMinutes', data=activity1)

ax.set_title('Scatterplot of calories and intense_activities')

Text(0.5, 1.0, 'Scatterplot of calories and intense_activities')

activity1.head(10) # cheking out the datset before transformation

	Id	ActivityDate	TotalSteps	TotalDistance	LoggedActivitiesDistance	VeryActiveDistance	ModeratelyActiveDistance	LightActiveDistance	SedentaryActiveDistance	VeryActiveMinutes	FairlyActiveMinutes	LightlyActiveMinutes	SedentaryMinutes	Calories	year	month	date	Week	Year	day
0	1503960366	2016-03-25	11004	7.11	0.0	2.57	0.46	4.07	0.0	33	12	205	804	1819	2016	3	25	12	2016	Friday
1	1503960366	2016-03-26	17609	11.55	0.0	6.92	0.73	3.91	0.0	89	17	274	588	2154	2016	3	26	12	2016	Saturday
2	1503960366	2016-03-27	12736	8.53	0.0	4.66	0.16	3.71	0.0	56	5	268	605	1944	2016	3	27	12	2016	Sunday
3	1503960366	2016-03-28	13231	8.93	0.0	3.19	0.79	4.95	0.0	39	20	224	1080	1932	2016	3	28	13	2016	Monday
4	1503960366	2016-03-29	12041	7.85	0.0	2.16	1.09	4.61	0.0	28	28	243	763	1886	2016	3	29	13	2016	Tuesday
5	1503960366	2016-03-30	10970	7.16	0.0	2.36	0.51	4.29	0.0	30	13	223	1174	1820	2016	3	30	13	2016	Wednesday
6	1503960366	2016-03-31	12256	7.86	0.0	2.29	0.49	5.04	0.0	33	12	239	820	1889	2016	3	31	13	2016	Thursday
7	1503960366	2016-04-01	12262	7.87	0.0	3.32	0.83	3.64	0.0	47	21	200	866	1868	2016	4	1	13	2016	Friday
8	1503960366	2016-04-02	11248	7.25	0.0	3.00	0.45	3.74	0.0	40	11	244	636	1843	2016	4	2	13	2016	Saturday
9	1503960366	2016-04-03	10016	6.37	0.0	0.91	1.28	4.18	0.0	15	30	314	655	1850	2016	4	3	13	2016	Sunday

activity1=activity1.drop(['Week','Year'],axis=1) # dropping the columns week and year

activity1.head(10) # cheking out the datset after transformation

	Id	ActivityDate	TotalSteps	TotalDistance	LoggedActivitiesDistance	VeryActiveDistance	ModeratelyActiveDistance	LightActiveDistance	SedentaryActiveDistance	VeryActiveMinutes	FairlyActiveMinutes	LightlyActiveMinutes	SedentaryMinutes	Calories	year	month	date	day
0	1503960366	2016-03-25	11004	7.11	0.0	2.57	0.46	4.07	0.0	33	12	205	804	1819	2016	3	25	Friday
1	1503960366	2016-03-26	17609	11.55	0.0	6.92	0.73	3.91	0.0	89	17	274	588	2154	2016	3	26	Saturday
2	1503960366	2016-03-27	12736	8.53	0.0	4.66	0.16	3.71	0.0	56	5	268	605	1944	2016	3	27	Sunday
3	1503960366	2016-03-28	13231	8.93	0.0	3.19	0.79	4.95	0.0	39	20	224	1080	1932	2016	3	28	Monday
4	1503960366	2016-03-29	12041	7.85	0.0	2.16	1.09	4.61	0.0	28	28	243	763	1886	2016	3	29	Tuesday
5	1503960366	2016-03-30	10970	7.16	0.0	2.36	0.51	4.29	0.0	30	13	223	1174	1820	2016	3	30	Wednesday
6	1503960366	2016-03-31	12256	7.86	0.0	2.29	0.49	5.04	0.0	33	12	239	820	1889	2016	3	31	Thursday
7	1503960366	2016-04-01	12262	7.87	0.0	3.32	0.83	3.64	0.0	47	21	200	866	1868	2016	4	1	Friday
8	1503960366	2016-04-02	11248	7.25	0.0	3.00	0.45	3.74	0.0	40	11	244	636	1843	2016	4	2	Saturday
9	1503960366	2016-04-03	10016	6.37	0.0	0.91	1.28	4.18	0.0	15	30	314	655	1850	2016	4	3	Sunday

activity1.shape # cheking the number of rows and columns in the transformed  dataset

(457, 18)

## plot the raw values 

col_select = ['Calories','VeryActiveMinutes','FairlyActiveMinutes','LightlyActiveMinutes','SedentaryMinutes']
wide_df = activity1[col_select]

# figure size
plt.figure(figsize=(15,8))

# timeseries plot using lineplot
ax = sns.lineplot(data=wide_df)

ax.set_title('Un-normalized value of calories and different activities based on activity minutes')

Text(0.5, 1.0, 'Un-normalized value of calories and different activities based on activity minutes')

# figure size
plt.figure(figsize=(15,8))

# Simple scatterplot between  calories burnt and total distance covered
ax = sns.scatterplot(x='Calories', y='TotalDistance', data=activity1)

ax.set_title('Scatterplot of calories and intense_activities')

Text(0.5, 1.0, 'Scatterplot of calories and intense_activities')

# figure size
plt.figure(figsize=(15,8))

# Simple scatterplot between calories burnt and the loggged activities distance
ax = sns.scatterplot(x='Calories', y='LoggedActivitiesDistance', data=activity1)

ax.set_title('Scatterplot of calories and intense_activities')

Text(0.5, 1.0, 'Scatterplot of calories and intense_activities')

# figure size
plt.figure(figsize=(15,8))

# Simple scatterplot between calories burnt and the distance of intense activies
ax = sns.scatterplot(x='Calories', y='VeryActiveDistance', data=activity1)

ax.set_title('Scatterplot of calories and intense_activities')

Text(0.5, 1.0, 'Scatterplot of calories and intense_activities')

# figure size
plt.figure(figsize=(15,8))

# Simple scatterplot between calories burnt and the distance of moderate activies
ax = sns.scatterplot(x='Calories', y='ModeratelyActiveDistance', data=activity1)

ax.set_title('Scatterplot of calories and intense_activities')

Text(0.5, 1.0, 'Scatterplot of calories and intense_activities')

# figure size
plt.figure(figsize=(15,8))

# Simple scatterplot
ax = sns.scatterplot(x='Calories', y='LightActiveDistance', data=activity1)

ax.set_title('Scatterplot of calories and intense_activities')

Text(0.5, 1.0, 'Scatterplot of calories and intense_activities')

## plot the raw values 

rol_select = ['TotalDistance','LoggedActivitiesDistance','VeryActiveDistance','ModeratelyActiveDistance', 'LightActiveDistance']
wide_df1 = activity1[rol_select]

# figure size
plt.figure(figsize=(15,8))

# timeseries plot using lineplot
ax = sns.lineplot(data=wide_df1)

ax.set_title('Un-normalized value of calories and different activities based on distance')

Text(0.5, 1.0, 'Un-normalized value of calories and different activities based on distance')

The EDA here gives us the insight about the relation between the active hours, the distance for which the user has moderate and intense activity and the calories burnt during that period.